Representation of texture structures with topological data analysis for stage IA lung adenocarcinoma in three-dimensional thoracic CT images
Representation of texture structures with topological data analysis for stage IA lung adenocarcinoma in three-dimensional thoracic CT images
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三维胸部 CT 图像中 IA 期肺腺癌纹理结构的拓扑数据分析表示
DOI：
10.1117/12.2581710
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Kaneko Masahiro
中科院分区：
文献类型：
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作者：
Kawata Yoshiki;Niki Noboru;Kusumoto Masahiko;Ohamatsu Hironobu;Aokage Keiju;Ishii Genichiro;Matsumoto Yuji;Tsuchida Takaaki;Eguchi Kenji;Kaneko Masahiro
捕获腺癌侵袭性的非侵入性生物标志物可以为精确医学提供关键的定量信息，以帮助临床决策。已知纹理特征用于测量肿瘤异质性，并且已被鉴定为与肺腺癌中的结果具有潜在相关性的特征。然而，当前用于分析由局部强度变化引起的纹理图案的方法限于揭示3D胸部CT图像中的纹理结构的空间配置。这种缺乏结节纹理的直观可视化使得理解肿瘤异质性的潜在含义成为一个具有挑战性的问题。在这项研究中，我们提出了一种方法相结合的结构纹理图像分解与拓扑数据分析来表示肺腺癌的纹理的空间配置。图像分解的目的是将3D胸部CT图像分成两个分量，即具有结节全局结构信息的分段平滑部分的结构分量和具有局部图案化振荡部分的纹理分量。我们演示了使用拓扑数据分析来捕获纹理组件所产生的建筑特征。具体来说，使用持久的同源性纹理组件，我们计算肺腺癌的拓扑表示与CT图像上的实变外观。将该方法应用于基于灰度共生矩阵（GLCM）等流行算法的纹理特征分级的早期肺腺癌的实例中，我们提出了具有拓扑数据分析的结构-纹理图像分解模型可能是分析3D胸部CT图像中肿瘤异质性的一种有前途的工具。
Noninvasive biomarkers that capture the adenocarcinoma aggressiveness could provide crucial quantitative information for precision medicine to aid clinical decision making. Texture features are known to measure tumor heterogeneity and have been identified as the features having a potential correlation to outcomes in lung adenocarcinomas. Nevertheless, current methods for analyzing texture patterns that arise from local intensity variation are limited to reveal a spatial configuration of the texture structures in 3D thoracic CT images. This lack of an intuitive visualization of the texture of nodules makes understanding the meanings underlying the tumor heterogeneity a challenging problem. In this study, we propose an approach combining a structure-texture image decomposition with a topological data analysis to represent a spatial configuration of the texture of lung adenocarcinoma. The image decomposition aims to split the 3D thoracic CT image into two components, namely, the structure component with the piecewise-smooth part having the global structural information of nodule and the texture component with the locally-patterned oscillating part. We demonstrate the use of topological data analysis to capture architectural features that arise from the texture component. Specifically, using persistent homology of texture components, we compute topological representations of lung adenocarcinomas with the appearance of consolidation on CT images. Applying the method to an example of early-stage lung adenocarcinomas graded with texture features based on the popular algorithm such as gray-level co-occurrence matrix (GLCM), we present that the structure-texture image decomposition model with topological data analysis might be a promising tool in analyzing the tumor heterogeneity in 3D thoracic CT images.